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Begriffe
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Geometric rotation

Spatial transformation applied to images that performs an angular rotation around a reference point, generally the center of the image, to generate variations in object orientation.

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Spatial translation

Horizontal or vertical movement of pixels in an image without altering their orientation, allowing to simulate different positions of objects in the frame.

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Homothetic scaling

Proportional modification of image dimensions by a uniform scale factor, preserving aspect ratios while changing the apparent size of objects.

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Shearing transformation

Geometric deformation that tilts the coordinate axes, creating a shearing effect that simulates oblique perspectives of objects in the image.

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Mirror flipping

Horizontal or vertical inversion of the image creating a mirror symmetry, essential for eliminating directional biases in machine learning.

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Random cropping

Random extraction of sub-regions of an image at different positions and scales, increasing cropping diversity and improving model robustness.

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Elastic deformation

Complex non-linear transformation applying variable local displacements to pixels, simulating organic and natural deformations of image structures.

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Perspective transformation

Geometric projection modifying the image perspective by simulating different viewpoints, crucial for learning perspective invariance.

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Affine transformation

Linear combination of transformations preserving parallelism and distance ratios, including rotation, translation, scaling, and shearing.

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Homography

Projective transformation between two planes preserving straight lines but not necessarily angles, used for complex viewpoint changes.

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Elastic warping

Fluid and continuous deformation of image structure based on displacement fields, simulating natural morphological variations of objects.

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Random zoom

Variable enlargement or reduction of image portions simulating different observation distances, essential for multi-scale learning.

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Geometric padding

Extension of image borders through specific methods (reflect, wrap, constant) before transformation to avoid artifacts and information loss.

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Bilinear interpolation

Pixel resampling method using the weighted average of the four nearest neighbors to estimate values after geometric transformation.

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Spatial Transformer Network

Learnable neural module dynamically applying geometric transformations to feature maps to normalize spatial representations.

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Geometric invariance

Property of a model to produce stable predictions in response to geometric transformations of input data, fundamental for generalization.

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Coordinate transformation matrix

2D/3D mathematical representation encoding geometric transformation parameters to systematically apply spatial deformations.

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Bounding box adaptation

Automatic adjustment of annotation bounding boxes concurrent with geometric transformations to maintain label consistency.

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Grid sampling

Regular grid sampling technique to apply variable local transformations, enabling complex and controlled geometric deformations.

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Rigid transformation

Combination of rotation and translation preserving distances and angles between points, used to simulate position changes without deformation.

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